Michael Recce – Tim Cook’s Dashboard

My guest this week is Michael Recce, the chief data scientist for Neuberger Berman. The topic of our conversation is the use of data in the investment process, to help cultivate what is commonly referred to as an information edge.

I call the episode “Tim Cook’s Dashboard” because of an interesting question that Michael poses: if you armed the best apple analyst in the world with Tim Cook’s private business dashboard, what might that be worth? Effectively Michael’s goal is to recreate the equivalent of a company dashboard for many businesses, helping analysts understand the fundamental health and direction of companies a bit better than the market does, and in so doing create an actionable edge.

This is a daunting task, and you will hear why. It requires both a fundamental understanding of business and of data, statistics, and methods like machine learning. In our own work, we’ve found machine learning to be useless for predicting future stock prices, but extremely useful for other things, like extracting and classifying data.

This conversation can get wonky at times, but as listeners know that is the best kind of conversation, even if it requires a second, slower listen. I hope you enjoy this talk with Michael Reece. Afterwards, I highly recommend you invest the time to read a series of posts called Machine Learning for Humans, which I will link to in the show notes. It helps demystify the buzz words and explain how these new technologies are being used.

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Books Referenced

Crossing the Chasm

One Two Three Infinity

Links Referenced

Sam Hinkie Podcast Episode

Show Notes

2:44 - (First Question) –  Changes in data science through the lens of Michael’s career

5:17 – The basic overview of using data and machine learning to create an edge

6:58 – How the state of business is more than just a single data point

7:53 – How you know when you’ve pulled a real signal from the noise of data

10:49 – The advantages that data provides

13:01 – Is there still an edge in decaying data

15:34 – Building data that would predict stock prices

19:43 – Prospectors vs miners in data mining

22:18 – Knowing when your prospectors are on to truth

27:09 – Understanding machine learning

30:10 – Defining partition

32:17 – Applying the parameters of selection process to stocks

36:05 – What’s the first step people could take to use data and machine learning to improve their investment process

38:54 – Building a sustainable advantage within data science

41:35 – Predicting the uncapped positive vs what’s seemingly easier, eliminating the negative

43:58 – How do we know to stop using a signal

46:22 – The importance of asking the right question

47:09 – Categories of objective functions that are interesting to measure data against

            47:42- Crossing the Chasm

48:37 – Most exciting things he’s found with data

51:17 – What investors, in